biglam/icdar2021-mapseg

ICDAR 2021 MapSeg — historical map segmentation

Map sheets from nine atlases of the City of Paris produced between 1894 and 1937 by the city's Map Service (Service du plan), annotated for three segmentation tasks. Built for the ICDAR 2021 Competition on Historical Map Segmentation and deposited on Zenodo (10.5281/zenodo.4817662).

This repository is a format conversion of that deposit — same images, same ground truth, repackaged so it loads with datasets instead of a 1.9 GB tarball.

The atlases were drawn at 1/5000 scale using trigonometric triangulation, roughly 20 sheets per year, for urban management and planning. Sheets are large: up to 10632 × 7711 pixels.

Three tasks, three configs

The tasks have different target shapes, so they are separate configs rather than one dataset with a task column.

Config	Task	Target	train / val / test
detect_building_blocks	find building blocks within map content	binary mask	1 / 1 / 3
segment_map_area	separate map content from title, legend, scale	binary mask	26 / 6 / 95
locate_graticule_intersections	locate coordinate grid crossings	list of [x, y] points	26 / 6 / 95

detect_building_blocks has five images in total. It was the competition's flagship task, but it is not a training set in any ordinary sense — treat it as a benchmark, not something to fit on.

The split sizes are inverted from the usual shape: 95 test sheets against 26 training sheets. That is how the competition was designed, and it is preserved here.

Structure

All three configs share sheet_id (the upstream three-digit identifier — 1NN train, 2NN validation, 3NN test) and image.

Config	Additional fields
detect_building_blocks	label (binary GT mask), area_mask (valid region; discard predictions outside it)
segment_map_area	label (binary GT mask)
locate_graticule_intersections	points, a list of [x, y] float coordinates

Masks are mode L with values strictly {0, 255}, and match their image dimensions exactly.

Caveats

Task 3 shares Task 2's images. The upstream archive does not duplicate them — the Task 3 folder contains only ground-truth CSVs. The conversion pulls the JPEGs across so the config is self-contained, which means the images appear twice across the repository and locate_graticule_intersections costs 1.77 GB of mostly redundant bytes. Load the config you need rather than the whole repo.

Points are float32. The upstream CSVs give sub-pixel coordinates to one decimal place; float32 storage rounds them, so 2373.2 reads back as 2373.19995. The error is around 5×10⁻⁵ px and irrelevant for evaluation, but exact string comparison against the source CSVs will not match.

Images are above Pillow's decompression-bomb threshold. At ~8×10⁷ pixels a sheet exceeds the default MAX_IMAGE_PIXELS limit and Pillow will refuse to decode it. Set PIL.Image.MAX_IMAGE_PIXELS = None before loading, as in the snippet below.

Do not score with your own metric. The competition's evaluation tools are released separately at icdar21-mapseg-eval, and published results used version 1.0.3. Numbers computed any other way are not comparable to the competition leaderboard.

One city, one map service, forty years. Every sheet comes from the same cartographic tradition and the same drawing conventions. Models fitted here should not be assumed to transfer to other map series.

Load

import PIL.Image
PIL.Image.MAX_IMAGE_PIXELS = None  # sheets run to ~8e7 px, over Pillow's default limit

from datasets import load_dataset

# stream — the test split alone is ~1.3 GB and sheets are very large
ds = load_dataset("biglam/icdar2021-mapseg", "segment_map_area", split="test", streaming=True)
ex = next(iter(ds))
ex["image"].size, ex["label"].size

Licence

CC BY 4.0, following the upstream deposit.

The underlying atlases are reproduced with permission granted by the City of Paris to the competition organisers. That permission covers this material as distributed; it is not a general release of the City of Paris map holdings. Original documents: Atlas municipal des vingt arrondissements de Paris, 1894–1937, Bibliothèque de l'Hôtel de Ville, City of Paris.

Credit

Data created by Joseph Chazalon, Edwin Carlinet and Thierry Géraud (EPITA Research and Development Laboratory), Yizi Chen, Julien Perret and Clément Mallet (Univ. Gustave Eiffel, IGN-ENSG, LaSTIG), and Bertrand Duménieu (LaDéHiS, CRH, EHESS). Partially funded by the French National Research Agency, project SoDuCo, grant ANR-18-CE38-0013. Images courtesy of the City of Paris.

Converted and repackaged for the Hub by Daniel van Strien.

@misc{chazalon.21.mapseg.data,
  author       = {Chazalon, Joseph and Carlinet, Edwin and Chen, Yizi and Perret, Julien and
                  Mallet, Cl{\'e}ment and Dum{\'e}nieu, Bertrand and G{\'e}raud, Thierry},
  title        = {{ICDAR 2021 Competition on Historical Map Segmentation --- Dataset}},
  howpublished = {online dataset},
  year         = {2021},
  doi          = {10.5281/zenodo.4817662}
}